A novel YOLO26-MoE optimized by an LLM agent for insulator fault detection considering UAV images
Authors: João Pedro Matos-Carvalho, Laio Oriel Seman, Stefano Frizzo Stefenon, Mohammad Khalaf Mohammad Khreasat, Gabriel Villarrubia González
Organizations: LASIGE, Faculdade de Ciˆencias, Universidade de Lisboa, 1749-016 Lisboa, Portugal · Department of Automation and Systems Engineering, Federal University of Santa Catarina, Florian´opolis, Brazil · Instituto Superior de Engenharia de Lisboa, Instituto Polit´ecnico de Lisboa, Rua Conselheiro Em´ıdio Navarro 1, 1959-007 Lisboa, Portugal · Expert Systems and Applications Lab, Faculty of Science, University of Salamanca, Plaza de los Ca´ıdos s/n, 37008 Salamanca, Spain
The inspection of electrical power line insulators is essential for ensuring grid reliability and preventing failures caused by damaged or degraded insulation components. In recent years, Unmanned Aerial Vehicles (UAVs) combined with deep learning-based vision systems have emerged as an effective solution for automating this process. However, insulator fault detection remains challenging due to small defect regions, heterogeneous fault patterns, complex backgrounds, and varying imaging conditions. To address these challenges, this paper proposes an optimized YOLO26-MoE, a novel object detection architecture that integrates a sparse Mixture-of-Experts (MoE) module into the high-resolution branch of the YOLO26 detector. The proposed modification enables adaptive feature refinement for subtle and diverse fault patterns while preserving the efficiency of a one-stage detection framework. Hyperparameter optimization, final training, and evaluation were coordinated through a tool-augmented Large Language Model (LLM) agent. The proposed model achieved 0.9900 mAP@0.5 and 0.9515 mAP@0.5:0.95, outperforming the latest YOLO versions. These results demonstrate that the proposed model provides an effective and reliable solution for UAV-based insulator fault detection.